Particle swarm optimization aided MIMO transceiver design

In this treatise, we design Particle Swarm Optimization (PSO) aided MIMO transceivers. The employment of multiple antennas leads to the concept of multiple-input multiple-output (MIMO) systems, which constitute an effective way of achieving an increased capacity. When multiple antennas are employed at the Base Station (BS), it is possible to employ Multiuser Detection (MUD) in the uplink. However, in the downlink (DL), due to the size as well as power consumption constraints of mobile devices, so-called Multiuser Transmission (MUT) techniques may be employed at the BS for suppressing the multiuser interference before transmissions, provided that the DL channel to be encountered may be accurately predicted.The MUT scheme using the classic MMSE criterion is popular owing to its simplicity. However, since the BER is the ultimate system performance indicator, in this treatise we are more interested in the Minimum BER MUT (MBER-MUT) design. Unlike the MBER-MUD, the MBER-MUT design encounters a constrained nonlinear optimization problem due to the associated total transmit power constraint. Sequential Quadratic Programming (SQP) algorithms may be used to obtain the precoder’s coefficients. However, the computational complexity of the SQP based MBER-MUT solution may be excessive for high-rate systems. Hence, as an attractive design alternative, continuous-valued PSO was invoked to find the MBER-MUT’s precoder matrix in order to reduce its computational complexity.Two PSO aided MBER-MUTs were designed and explained. The first one may be referred to as a symbol-specific MBER-MUT, while the other one may be termed as the average MBER-MUT. Our simulation results showed that both of our designs achieve an improvement in comparison to conventional linear MUT schemes, while providing a reduced complexity compared to the state-of-art SQP based MBER-MUT. Later, we introduced discrete multi-valued PSO into the context of MMSE Vector Precoding (MMSEVP) to find the optimal perturbation vector. As a nonlinearMUT scheme, the VP provides an attractive BER performance. However, the computational complexity imposed during the search for optimal perturbation vector may be deemed excessive, hence it becomes necessary to find reduced-complexity algorithms while maintaining a reasonable BER performance. Lattice-Reduction-aied (LRA) VP is the most popular approach to reduce the complexity imposed. However, the LRA VP is only capable of achieving a suboptimum BER performance, although its complexity is reduced. Another drawback of LRA VP is that its complexity is fixed, which is beneficial for real-time implenebtations, but it is unable to strike a trade-off between the target BER and its required complexity. Therefore, we developed a discrete multi-valued PSO aided MMSE-VP design, which has a flexible complexity and it is capable of iteratively improving the achievable.In Chapter 5, our contributions in the field of Minimum Bit Error Rate Vector Precoding (MBER-VP) are unveiled. Zero-Forcing Vector Precoding (ZF-VP) and MMSE Vector Precoding (MMSE-VP) had already been proposed in the literature. However, to the best of our knowledge, no VP algorithm was proposed to date based on the direct minimisation of the BER. Our improved MMSE-VP design based on the MBER criterion first invokes a regularised channel inversion technique and then superimposes a discrete-valued perturbation vector for minimising the BER of the system. To further improve the system’s BER performance, an MBER-based generalised continuous-valued VP algorithm was also proposed. Assuming the knowledge of the information symbol vector and the CIR matrix, we consider the generation of the effective symbol vector to be transmitted by directly minimising the BER of the system. Our simulation results show the advantage of these two VP schemes based on the MBER criterion, especially for rank-deficient systems, where the number of BS transmit antennas is lower than the number of MSs supported. The robustness of these two designs to the CIR estimation error are also investigated. Finally, the computational complexity imposed is also quantified in this chapter.With the understanding of the BER criterion of VP schemes, we then considered a new transceiver design by combing uniform channel decomposition and MBER vector precoding, which leads to a joint transmitter and receiver design referred as the UCD-MBER-VP scheme. In our proposed UCD-MBER-VP scheme, the precoding and equalisation matrices are calculated by the UCD method, while the perturbation vector is directly chosen based on the MBER criterion. We demonstrated that the proposed algorithm outperforms the existing benchmark schemes, especially for rank-deficient systems, where the number of users supported is more than the number of transmit antennas employed. Moreover, our proposed joint design approach imposes a similar computational complexity as the existing benchmark schemes